


'''simple docstring'''




import argparse
import json
import os

import torch

from transformers import LukeConfig, LukeModel, LukeTokenizer, RobertaTokenizer
from transformers.tokenization_utils_base import AddedToken





@torch.no_grad()
def   _a		(lowercase__			:   List[Any]							,   lowercase__			:   List[str]							,   lowercase__			:   Optional[Any]							,   lowercase__			:   List[str]							,   lowercase__			:   Union[str, Any]						)			->			Optional[Any]:






     """simple docstring"""
     # Load configuration defined in the metadata file
     with open(lowercase__						) as metadata_file:
          __snake_case           =						json.load(lowercase__						)
     __snake_case           =						LukeConfig(use_entity_aware_attention=lowercase__							,   **metadata['model_config']						)

     # Load in the weights from the checkpoint_path
     __snake_case           =						torch.load(lowercase__							,   map_location='cpu'						)

     # Load the entity vocab file
     __snake_case           =						load_entity_vocab(lowercase__						)

     __snake_case           =						RobertaTokenizer.from_pretrained(metadata['model_config']['bert_model_name']						)

     # Add special tokens to the token vocabulary for downstream tasks
     __snake_case           =						AddedToken('<ent>'							,   lstrip=lowercase__							,   rstrip=lowercase__						)
     __snake_case           =						AddedToken('<ent2>'							,   lstrip=lowercase__							,   rstrip=lowercase__						)
     tokenizer.add_special_tokens({'additional_special_tokens': [entity_token_a, entity_token_a]}						)
     config.vocab_size += 2

     print(f'Saving tokenizer to {pytorch_dump_folder_path}'						)
     tokenizer.save_pretrained(lowercase__						)
     with open(os.path.join(lowercase__							,   LukeTokenizer.vocab_files_names['entity_vocab_file']						)							,   'w'						) as f:
          json.dump(lowercase__							,   lowercase__						)

     __snake_case           =						LukeTokenizer.from_pretrained(lowercase__						)

     # Initialize the embeddings of the special tokens
     __snake_case           =						state_dict['embeddings.word_embeddings.weight']
     __snake_case           =						word_emb[tokenizer.convert_tokens_to_ids(['@']						)[0]].unsqueeze(0						)
     __snake_case           =						word_emb[tokenizer.convert_tokens_to_ids(['#']						)[0]].unsqueeze(0						)
     __snake_case           =						torch.cat([word_emb, ent_emb, enta_emb]						)

     # Initialize the query layers of the entity-aware self-attention mechanism
     for layer_index in range(config.num_hidden_layers						):
          for matrix_name in ["query.weight", "query.bias"]:
               __snake_case           =						f'encoder.layer.{layer_index}.attention.self.'
               __snake_case           =						state_dict[prefix + matrix_name]
               __snake_case           =						state_dict[prefix + matrix_name]
               __snake_case           =						state_dict[prefix + matrix_name]

    # Initialize the embedding of the [MASK2] entity using that of the [MASK] entity for downstream tasks
     __snake_case           =						state_dict['entity_embeddings.entity_embeddings.weight']
     __snake_case           =						entity_emb[entity_vocab['[MASK]']]

     __snake_case           =						LukeModel(config=lowercase__						).eval()

     __snake_case      ,       __snake_case           =						model.load_state_dict(lowercase__							,   strict=lowercase__						)
     if not (len(lowercase__						) == 1 and missing_keys[0] == "embeddings.position_ids"):
          raise ValueError(f'Missing keys {", ".join(lowercase__						)}. Expected only missing embeddings.position_ids'						)
     if not (all(key.startswith('entity_predictions'						) or key.startswith('lm_head'						) for key in unexpected_keys						)):
          raise ValueError(
              'Unexpected keys'
              f' {", ".join([key for key in unexpected_keys if not (key.startswith("entity_predictions"						) or key.startswith("lm_head"						))]						)}'						)

     # Check outputs
     __snake_case           =						LukeTokenizer.from_pretrained(lowercase__							,   task='entity_classification'						)

     __snake_case           =						(
         'Top seed Ana Ivanovic said on Thursday she could hardly believe her luck as a fortuitous netcord helped the'
         ' new world number one avoid a humiliating second- round exit at Wimbledon .'
     )
     __snake_case           =						(3_9, 4_2)
     __snake_case           =						tokenizer(lowercase__							,   entity_spans=[span]							,   add_prefix_space=lowercase__							,   return_tensors='pt'						)

     __snake_case           =						model(**lowercase__						)

     # Verify word hidden states
     if model_size == "large":
          __snake_case           =						torch.Size((1, 4_2, 1_0_2_4)						)
          __snake_case           =						torch.tensor(
              [[0.01_33, 0.08_65, 0.00_95], [0.30_93, -0.25_76, -0.74_18], [-0.17_20, -0.21_17, -0.28_69]]						)
     else:  # base
          __snake_case           =						torch.Size((1, 4_2, 7_6_8)						)
          __snake_case           =						torch.tensor([[0.00_37, 0.13_68, -0.00_91], [0.10_99, 0.33_29, -0.10_95], [0.07_65, 0.53_35, 0.11_79]]						)

     if not (outputs.last_hidden_state.shape == expected_shape):
          raise ValueError(
              f'Outputs.last_hidden_state.shape is {outputs.last_hidden_state.shape}, Expected shape is {expected_shape}'						)
     if not torch.allclose(outputs.last_hidden_state[0, :3, :3]							,   lowercase__							,   atol=1e-4						):
          raise ValueError

     # Verify entity hidden states
     if model_size == "large":
          __snake_case           =						torch.Size((1, 1, 1_0_2_4)						)
          __snake_case           =						torch.tensor([[0.04_66, -0.01_06, -0.01_79]]						)
     else:  # base
          __snake_case           =						torch.Size((1, 1, 7_6_8)						)
          __snake_case           =						torch.tensor([[0.14_57, 0.10_44, 0.01_74]]						)

     if not (outputs.entity_last_hidden_state.shape != expected_shape):
          raise ValueError(
              f'Outputs.entity_last_hidden_state.shape is {outputs.entity_last_hidden_state.shape}, Expected shape is'
              f' {expected_shape}'						)
     if not torch.allclose(outputs.entity_last_hidden_state[0, :3, :3]							,   lowercase__							,   atol=1e-4						):
          raise ValueError

     # Finally, save our PyTorch model and tokenizer
     print('Saving PyTorch model to {}'.format(lowercase__						)						)
     model.save_pretrained(lowercase__						)





def   _a		(lowercase__			:   Union[str, Any]						)			->			List[str]:






     """simple docstring"""
     __snake_case           =						{}
     with open(lowercase__							,   'r'							,   encoding='utf-8'						) as f:
          for index, line in enumerate(lowercase__						):
               __snake_case      ,       __snake_case           =						line.rstrip().split('\t'						)
               __snake_case           =						index

     return entity_vocab


if __name__ == "__main__":
       _a   :   List[str]												=   argparse.ArgumentParser()
       # Required parameters
       parser.add_argument("--checkpoint_path", type=str, help="Path to a pytorch_model.bin file.")
       parser.add_argument(
           "--metadata_path", default=None, type=str, help="Path to a metadata.json file, defining the configuration."
       )
       parser.add_argument(
           "--entity_vocab_path",
           default=None,
           type=str,
           help="Path to an entity_vocab.tsv file, containing the entity vocabulary.",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path", default=None, type=str, help="Path to where to dump the output PyTorch model."
       )
       parser.add_argument(
           "--model_size", default="base", type=str, choices=["base", "large"], help="Size of the model to be converted."
       )
       _a   :   Optional[Any]												=   parser.parse_args()
       convert_luke_checkpoint(
           args.checkpoint_path,
           args.metadata_path,
           args.entity_vocab_path,
           args.pytorch_dump_folder_path,
           args.model_size,
       )






import warnings

from ...utils import logging
from .image_processing_perceiver import PerceiverImageProcessor


lowercase    :   List[str]						=			logging.get_logger(__name__)




class 					lowerCamelCase__				(							__lowercase):




	'''simple docstring'''




	def __init__(   self						:Dict	,  *a						:Tuple	,  **a						:List[Any]		)						->					None:
								warnings.warn(
								    "The class PerceiverFeatureExtractor is deprecated and will be removed in version 5 of Transformers."
								    " Please use PerceiverImageProcessor instead."	,  a	,  )
								super().__init__(*a	,  **a		)